Yi-Heng Zhu

TL;DR: This paper establishes a novel computational method, named TargetDBP, for accurately targeting DBPs from primary sequences, and constructs a new gold-standard and non-redundant benchmark dataset from PDB database to evaluate and compare the proposed targetDBP with other existing predictors.

TL;DR: A two-stage imbalanced learning algorithm, called ensembled hyperplane-distance-based support vector machines (E-HDSVM), to improve the prediction performance of protein-DNA binding sites and an enhanced AdaBoost algorithm to ensemble multiple trained SVMs, which overcomes the overfitting problem.

TL;DR: A new machine-learning-based pipeline that uses a newly developed deep-cascade forest (DCF) model with multiple types of sequence-based features to predict protein crystallization propensity is developed, which improves crystallization recognition by incorporating sequence-order information with solvent accessibility of residues.

TL;DR: Wang et al. as mentioned in this paper proposed a new feature encoding method, termed weight attenuation position-specific scoring matrix (WAPSSM), which builds upon the protein evolutionary information.

TL;DR: A novel deep-learning model, called Combination of Multi-Scale Convolutional Network and Long Short-Term Memory Network (MCNN-LSTM), which utilizes multi-scale convolution for feature processing, and the long short-term memory network to recognize TFBS in DNA sequences is proposed.